# Repeated Observations for Classification

arXiv: https://arxiv.org/abs/2307.09896 · HTML: https://mlsanity.com/paper/2307.09896/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 19 Jul 2023.

## Builds on (most cited first, 1 of 1)

- [Copula-based anomaly scoring and localization for large-scale, high-dimensional continuous data](https://mlsanity.com/paper/1912.02166/) (2019)

## Abstract (arXiv)

We study the problem nonparametric classification with repeated observations. Let bX be the d dimensional feature vector and let Y denote the label taking values in 1,dots ,M. In contrast to usual setup with large sample size n and relatively low dimension d, this paper deals with the situation, when instead of observing a single feature vector bX we are given t repeated feature vectors bV1,dots ,bVt . Some simple classification rules are presented such that the conditional error probabilities have exponential convergence rate of convergence as t→∞. In the analysis, we investigate particular models like robust detection by nominal densities, prototype classification, linear transformation, linear classification, scaling.